forked from mindspore-Ecosystem/mindspore
remove internal interface in wide&deep
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parent
9dd4ab0e3e
commit
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mindspore/parallel
model_zoo/official/recommend
wide_and_deep
wide_and_deep_multitable/src
tests
st/model_zoo_tests/wide_and_deep/python_file_for_ci
ut/python/parallel
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@ -17,5 +17,7 @@ This interface is ONLY used in Auto-parallel procedure.
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"""
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from .algo_parameter_config import get_algo_parameters, reset_algo_parameters, \
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set_algo_parameters
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from ._cost_model_context import set_multi_subgraphs, get_multi_subgraphs
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__all__ = ["get_algo_parameters", "reset_algo_parameters", "set_algo_parameters"]
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__all__ = ["set_multi_subgraphs", "get_multi_subgraphs",
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"get_algo_parameters", "reset_algo_parameters", "set_algo_parameters"]
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@ -479,7 +479,6 @@ set_cost_model_context_func_map = {
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"costmodel_communi_threshold": cost_model_context().set_costmodel_communi_threshold,
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"costmodel_communi_const": cost_model_context().set_costmodel_communi_const,
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"costmodel_communi_bias": cost_model_context().set_costmodel_communi_bias,
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"multi_subgraphs": cost_model_context().set_multi_subgraphs,
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"run_phase": cost_model_context().set_run_phase,
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"costmodel_allreduce_fusion_algorithm": cost_model_context().set_costmodel_allreduce_fusion_algorithm,
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"costmodel_allreduce_fusion_times": cost_model_context().set_costmodel_allreduce_fusion_times,
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@ -501,7 +500,6 @@ get_cost_model_context_func_map = {
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"costmodel_communi_threshold": cost_model_context().get_costmodel_communi_threshold,
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"costmodel_communi_const": cost_model_context().get_costmodel_communi_const,
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"costmodel_communi_bias": cost_model_context().get_costmodel_communi_bias,
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"multi_subgraphs": cost_model_context().get_multi_subgraphs,
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"run_phase": cost_model_context().get_run_phase,
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"costmodel_allreduce_fusion_algorithm": cost_model_context().get_costmodel_allreduce_fusion_algorithm,
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"costmodel_allreduce_fusion_times": cost_model_context().get_costmodel_allreduce_fusion_times,
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@ -538,7 +536,6 @@ def set_cost_model_context(**kwargs):
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costmodel_communi_threshold (float): A parameter used in adjusting communication calculation for practice.
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costmodel_communi_const (float): A parameter used in adjusting communication calculation for practice.
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costmodel_communi_bias (float): A parameter used in adjusting communication calculation for practice.
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multi_subgraphs (bool): A parameter used in marking the flag of ANF graph containing multiple subgraphs.
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run_phase (int): A parameter indicating which phase is running: training (0) or inference (1). Default: 0.
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costmodel_allreduce_fusion_algorithm (int): The allreduce fusion algorithm.
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0: bypass allreduce fusion;
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@ -591,3 +588,18 @@ def get_cost_model_context(attr_key):
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def reset_cost_model_context():
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"""Reset cost model context attributes."""
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cost_model_context().reset_cost_model()
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def set_multi_subgraphs(multi_subgraph=True):
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"""
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Set the flag of ANF graph containing multiple subgraphs.
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Args:
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multi_subgraph (bool): A parameter used in marking the multi-subgraphs flag.
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"""
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cost_model_context().set_multi_subgraphs(multi_subgraph)
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def get_multi_subgraphs():
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"""
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Get the flag of ANF graph containing multiple subgraphs.
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"""
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cost_model_context().get_multi_subgraphs()
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@ -14,7 +14,7 @@
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# ============================================================================
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"""wide and deep model"""
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import numpy as np
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from mindspore import nn
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from mindspore import nn, context
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from mindspore import Parameter, ParameterTuple
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import mindspore.common.dtype as mstype
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from mindspore.ops import functional as F
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@ -22,10 +22,7 @@ from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from mindspore.nn import Dropout
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from mindspore.nn.optim import Adam, FTRL, LazyAdam
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# from mindspore.nn.metrics import Metric
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from mindspore.common.initializer import Uniform, initializer
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# from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
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from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_mirror_mean
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from mindspore.train.parallel_utils import ParallelMode
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from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
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from mindspore.communication.management import get_group_size
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@ -142,7 +139,7 @@ class WideDeepModel(nn.Cell):
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self.batch_size = config.batch_size
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host_device_mix = bool(config.host_device_mix)
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parameter_server = bool(config.parameter_server)
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parallel_mode = _get_parallel_mode()
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parallel_mode = context.get_auto_parallel_context("parallel_mode")
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is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
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if is_auto_parallel:
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self.batch_size = self.batch_size * get_group_size()
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@ -259,7 +256,7 @@ class NetWithLossClass(nn.Cell):
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super(NetWithLossClass, self).__init__(auto_prefix=False)
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host_device_mix = bool(config.host_device_mix)
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parameter_server = bool(config.parameter_server)
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parallel_mode = _get_parallel_mode()
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parallel_mode = context.get_auto_parallel_context("parallel_mode")
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is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
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self.no_l2loss = (is_auto_parallel if host_device_mix else parameter_server)
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self.network = network
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@ -312,7 +309,7 @@ class TrainStepWrap(nn.Cell):
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def __init__(self, network, sens=1024.0, host_device_mix=False, parameter_server=False):
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super(TrainStepWrap, self).__init__()
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parallel_mode = _get_parallel_mode()
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parallel_mode = context.get_auto_parallel_context("parallel_mode")
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is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
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self.network = network
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self.network.set_train()
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@ -351,12 +348,11 @@ class TrainStepWrap(nn.Cell):
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self.reducer_flag = False
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self.grad_reducer_w = None
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self.grad_reducer_d = None
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parallel_mode = _get_parallel_mode()
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self.reducer_flag = parallel_mode in (ParallelMode.DATA_PARALLEL,
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ParallelMode.HYBRID_PARALLEL)
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if self.reducer_flag:
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mean = _get_mirror_mean()
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degree = _get_device_num()
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mean = context.get_auto_parallel_context("mirror_mean")
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degree = context.get_auto_parallel_context("device_num")
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self.grad_reducer_w = DistributedGradReducer(self.optimizer_w.parameters, mean, degree)
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self.grad_reducer_d = DistributedGradReducer(self.optimizer_d.parameters, mean, degree)
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@ -22,7 +22,7 @@ from mindspore import Model, context
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
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from mindspore.train import ParallelMode
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from mindspore.communication.management import get_rank, get_group_size, init
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from mindspore.parallel import _cost_model_context as cost_model_context
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from mindspore.parallel import set_multi_subgraphs
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from mindspore.nn.wrap.cell_wrapper import VirtualDatasetCellTriple
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from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
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@ -127,7 +127,7 @@ if __name__ == "__main__":
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context.set_context(mode=context.GRAPH_MODE, device_target=wide_deep_config.device_target, save_graphs=True)
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context.set_context(variable_memory_max_size="24GB")
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context.set_context(enable_sparse=True)
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cost_model_context.set_cost_model_context(multi_subgraphs=True)
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set_multi_subgraphs()
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if wide_deep_config.device_target == "Ascend":
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init("hccl")
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elif wide_deep_config.device_target == "GPU":
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@ -16,7 +16,7 @@
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import numpy as np
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import mindspore.common.dtype as mstype
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from mindspore import nn
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from mindspore import nn, context
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from mindspore import Tensor, Parameter, ParameterTuple
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from mindspore.ops import functional as F
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from mindspore.ops import composite as C
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@ -24,7 +24,6 @@ from mindspore.ops import operations as P
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from mindspore.nn import Dropout, Flatten
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from mindspore.nn.optim import Adam, FTRL
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from mindspore.common.initializer import Uniform, initializer
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from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_mirror_mean
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from mindspore.train.parallel_utils import ParallelMode
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from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
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@ -552,13 +551,13 @@ class TrainStepWrap(nn.Cell):
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self.reducer_flag = False
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self.grad_reducer_w = None
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self.grad_reducer_d = None
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parallel_mode = _get_parallel_mode()
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parallel_mode = context.get_auto_parallel_context("parallel_mode")
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if parallel_mode in (ParallelMode.DATA_PARALLEL,
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ParallelMode.HYBRID_PARALLEL):
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self.reducer_flag = True
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if self.reducer_flag:
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mean = _get_mirror_mean()
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degree = _get_device_num()
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mean = context.get_auto_parallel_context("mirror_mean")
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degree = context.get_auto_parallel_context("device_num")
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self.grad_reducer_w = DistributedGradReducer(
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self.optimizer_w.parameters, mean, degree)
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self.grad_reducer_d = DistributedGradReducer(
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@ -21,7 +21,7 @@ from mindspore import Model, context
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from mindspore.train.callback import TimeMonitor
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from mindspore.train import ParallelMode
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from mindspore.communication.management import get_rank, get_group_size, init
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from mindspore.parallel import _cost_model_context as cost_model_context
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from mindspore.parallel import set_multi_subgraphs
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from mindspore.nn.wrap.cell_wrapper import VirtualDatasetCellTriple
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from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
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@ -33,7 +33,7 @@ from src.config import WideDeepConfig
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
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context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, mirror_mean=True)
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cost_model_context.set_cost_model_context(multi_subgraphs=True)
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set_multi_subgraphs()
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init()
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@ -23,7 +23,7 @@ from mindspore.nn.optim import Adam, FTRL
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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from mindspore.parallel import _cost_model_context as cost_model_context
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from mindspore.parallel import set_multi_subgraphs
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from mindspore.parallel._utils import _reset_op_id as reset_op_id
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@ -103,7 +103,7 @@ class TrainStepWarp(nn.Cell):
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def test_double_subgraphs():
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cost_model_context.set_cost_model_context(multi_subgraphs=True)
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set_multi_subgraphs()
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context.set_context(save_graphs=True)
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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